Data Science for Information Professionals provides an accessible introduction to
a potentially complex field, providing readers with an overview of data science
and a framework for its application. It provides detailed examples and analysis
on real data sets to explore the basics of the subject in three principle
areas: clustering and social network analysis; predictions and forecasts; and
text analysis and mining.
As well as highlighting a wealth of user-friendly data science
tools, the book also includes some example code in two of the most popular
programming languages (R and Python) to demonstrate the ease with
which the information professional can move beyond the graphical user interface
and achieve significant analysis with just a few lines of code.
After reading, readers will
the growing importance of data science
the role of the information professional in
some of the most important tools and methods
that information professionals can use.
Bringing together the growing importance of data science and the
increasing role of information professionals in the management and use of data,
Practical Data Science for Information
Professionals will provide a practical introduction to the topic
specifically designed for the information community. It
will appeal to librarians and information professionals all around the world,
from large academic libraries to small research libraries. By focusing on the
application of open source software, it aims to reduce barriers for readers to
use the lessons learned within.